Going beyond recommendations: Where next for data driven design?
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Your phone already knows you better than your family and friends. As AI and machine learning algorithms crunch through the huge datasets we generate our online personas definitely knows you better than you know yourself. What opportunities does this give us to design? How can we design with these datasets to improve users lives beyond just simple recommendations? How do we avoid the pitfalls and shift the boundaries of the new normal highly personalised world?
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Going beyond recommendations: Where next for data driven design?
- 2. WHERE NEXT FOR DATA DRIVEN DESIGN?
- 4. DATA IS THE FUEL FOR THE NEXT GENERATION OF PERSONALISED SERVICES
- 5. 1. DESIGN WITH DATA
- 6. 1. DESIGN WITH DATA 2. DESIGN BY DATA
- 7. 1. DESIGN WITH DATA 2. DESIGN BY DATA 3. DESIGN FOR DATA MORE INFO HTTPS://PROJECTSBYIF.COM/BLOG/DESIGN-FOR-DATA HTTPS://WWW.SLIDESHARE.NET/FJORDNET/FJORD-EQUINOX-DATA-DESIGN
- 8. WHY NOW?
- 10. WHAT I WANT, WHEN I WANT IT, HOW I LIKE IT
- 12. FLUID JOURNEYS
- 13. IOT ALL THE THINGS
- 14. DIGITAL & PHYSICAL CHANNELS HYPER PERSONALISED SERVICES BIG DATA & MACHINE LEARNING + = WE HOPE
- 15. PERSONALISATION IN LUXURY FASHION *Completed whilst at Code and Theory
- 16. DATA TO INSPIRE SERVICES
- 17. THICK DATA BIG DATA WIDE DATA WHO, WHY & HOW
- 18. THICK DATA = QUALITATIVE STUDIES BIG DATA = AN EXTREMELY LARGE DATA SET WIDE DATA = INDUSTRY TRENDS & PUBLIC DATA SETS Wide data can also be big data depending on the size of the data set Big data here refers to your product, marketing and customer data
- 19. PERSONAL SHOPPER INTERVIEWS STAKEHOLDER INTERVIEWS USER INTERVIEWS THICK DATA (QUALITATIVE STUDIES)
- 20. THICK DATA (QUALITATIVE STUDIES) VOC SURVEY SHOPPING PATTERNS PERSONAL SHOPPER INTERVIEWS STAKEHOLDER INTERVIEWS USER INTERVIEWS BIG DATA (DATA YOU HAVE GATHERED) READING PATTERNS
- 21. VOC SURVEY SHOPPING PATTERNS PERSONAL SHOPPER INTERVIEWS INDUSTRY TRENDS SOCIAL HABITSCOMPETITOR ANALYSIS STAKEHOLDER INTERVIEWS SEARCH TRENDS USER INTERVIEWS WIDE DATA (INDUSTRY TRENDS, COMPETITOR ANALYSIS DIRECT INDIRECT AND PERCEPTUAL, SEARCH TRENDS AND SOCIAL MEDIA HABITS) READING PATTERNS THICK DATA (QUALITATIVE STUDIES) BIG DATA (DATA YOU HAVE GATHERED)
- 22. THE INSIGHT WH O N OW
- 23. THE INSIGHT WHO NEXTWH O N OW
- 24. THE INSIGHT WHAT TO SAYWHO NEXTWH O N OW
- 25. THE INSIGHT HOW TO SAY ITWHAT TO SAYWHO NEXTWH O N OW
- 26. THE INSIGHT WHE RE AND WHENHOW TO SAY ITWHAT TO SAYWHO NEXTWH O N OW
- 28. CLOTHES ARE FUNDAMENTAL TO USERS'S SENSE OF IDENTITY. FASHION BUYING BEHAVIOURS SHIFT DEPENDING ON THE USERS CONTEXT.
- 32. CONTENT THAT SUPPORTS & INSPIRES FASHION CONFIDENCE AND DRIVES COMMERCE
- 33. PUBLISHING MODEL
- 34. WHEN TO PUBLISH Typical Weekday Typical Weekend SOCIAL MEDIA BUYING PEAKS
- 35. PERSONALISATION AS UNIQUE AS THEIR CUSTOMERS https://econsultancy.com/blog/68219-four- things-brands-can-learn-about-content-marketing- from-net-a-porter
- 36. DATA DRIVEN STRATEGY FOR PERSONALISATION, CONTENT CREATION & DISTRIBUTION
- 37. WHO IS DOING IT WELL?
- 38. GOOGLE
- 39. HAS GONE FROM SIMPLY ORGANISING THE WORLDS INFORMATION TO HARNESSING IT
- 41. NETFLIX
- 42. PERSONALISED RANKING, TOP-N RANKING, TRENDING NOW, CONTINUE WATCHING, VIDEO - VIDEO SIMILARITY, PERSONALISED PAGE GENERATION, SEARCH, PERSONALISED IMAGE SELECTION
- 43. DATA DRIVES THE PRODUCT & THE CONTENT
- 44. THE CHALLENGES WHY HAVEN’T WE ALL GOT THERE?
- 45. GETTING THE DATA
- 46. DON’T UNDERSTAND HOW PERSONAL INFORMATION IS USED 92% DO NOT TRUST ORGANISATIONS TO USE DATA RESPONSIBLY 57% http://www.bbc.co.uk/news/business-37476335
- 47. OFTEN COMBING MULTIPLE SOURCES OF DATA GIVE FAR MORE INSIGHT THAN ONE SET ALONE
- 49. “ALL DATA IN AGGREGATE IS CRAP.” GOOGLE ANALYTICS EVANGELIST AVINASH KAUSHIK
- 50. MACHINE LEARNING LOVES FINDING PATTERNS
- 52. MACHINES WILL INCREASINGLY BE MAKING DECISIONS WITHIN USER EXPECTATIONS
- 53. “HUMAN-CENTERED DESIGN HAS EXPANDED FROM THE DESIGN OF OBJECTS (INDUSTRIAL DESIGN) TO THE DESIGN OF EXPERIENCES (ADDING INTERACTION DESIGN, VISUAL DESIGN, AND THE DESIGN OF SPACES) AND THE NEXT STEP WILL BE THE DESIGN OF SYSTEM BEHAVIOR: THE DESIGN OF THE ALGORITHMS THAT DETERMINE THE BEHAVIOR OF AUTOMATED OR INTELLIGENT SYSTEMS,” HARRY WEST AT FROG
- 55. WHAT IF? WHAT IF EVERYONE IN THE WORLD USED YOUR SERVICE HOW WOULD IT CHANGE SOCIETY? WHAT IS THE WORST THING THAT COULD HAPPEN IF IT GOES WRONG?
- 56. THE 5 THINGS TO REMEMBER
- 57. 1. DATA DOESN'T = INSIGHT 2. MATCH THICK DATA WITH BIG DATA & WIDE DATA 3. SET UP A DATA / MEASUREMENT STRATEGY 4. HYPOTHESISE & TEST (RIGHT DATA, RIGHT TIME) 5. BE TRANSPARENT & RESPONSIBLE WITH DATA 5 THINGS TO REMEMBER @hollielubbock